Learning Image Classification Method Based On Distance Measurement

Content-based image classification is one of the hot and knotty issues in computer vision. Recently, almost all current approaches rely on distance between low-level features for judging semantic similarity, and then understand the content of image. There is, however, a deep semantic gap between low-level features and high-level concepts. The topic on how to bridge the semantic gap effectively and improve the accuracy of image classification has been widely explored. Moreover, the subjectivity in similarity needs to be incorporated more rigorously into image similarity measures, to achieve what can be called'personalized'image classification.In this paper, a general framework of image classification system and extraction of low-level feature, especially distance measures of various features are introduced. Some distance measures have poor performance in the task of classification, since they fail to describe the feature space and neglect the help of contextual information. Hence, the key point is how to learn a proper distance metric automatically from the feature space of labeled images. This paper analyzes the methods on distance metric learning, and explores the nature and connection between them.Then, we propose an image classification method based on feature complement ratio matrix, which tries to bridge the semantic gap from two aspects. On the one hand, different low-level feature describes different characteristic of the content of image, so a better representation can be obtained by fusing these features. A visual feature complement ratio matrix is been computed, based on which the fusion feature set can be selected effectually. On the other hand, a proper distance metric, which reflects the semantic similarity between images, is obtained by using a distance metric learning algorithm. Experiments show that our method can improve the accuracy of image classification effectively.In addition, some experiments have been conducted on analyzing the effect of different division of image set and the performance of several classification methods, local feature and ten MPEG-7 visual descriptor in the task of image classification.